VLOAM (Visual-LiDAR Odometry And Mapping) [1]} uses LiDAR data to get the 3D information of the visual features. Pose estimates from visual feature matching and LiDAR scan matching are combined in graph optimization. The VIL-SLAM, presented by Shao et al. in 2019 [2]} combines a visual-inertial odometry (VIO) frontend with LiDAR-mapping and LiDAR-enhanced loop closure. Both algorithms demonstrated state-of-the-art precision under normal conditions with improved robustness against adverse conditions.
The idea to of combining the ORB-SLAM3 with LiDAR data had previously been presented in [3]}, however, no different upsampling methods were presented and a scientific analysis and discussion of the results were missing.
